Acknowledgments
I appreciate help equally from the people listed below. They are
Professor Wenjun Bu; Professor Lin Liu; Ph.D. student Hua Wang;
Master’s student Yu Sun and Deshui Yu from College of Life
Sciences, Nankai University; Professor Jishou Ruan; PhD student
Zhenfeng Wu from School of Mathematical Sciences, Nankai Uni-
versity; and Associate Professor Weixiang Liu from Shenzhen
University.References
- Gao S, Ou J, Xiao K (2014) R language and
Bioconductor in bioinformatics applications
(Chinese Edition). Tianjin Science and Technol-
ogy Translation Publishing, Co. Ltd, Tianjin - Stegle O, Teichmann SA, Marioni JC (2015)
Computational and analytical challenges in
single-cell transcriptomics. Nat Rev Genet 16
(3):133–145 - Zhang M, Sun H, Fei Z, Zhan F, Gong X, Gao
S (2014) Fastq_clean: an optimized pipeline to
clean the Illumina sequencing data with quality
control. 2014 I.E. international conference on
bioinformatics and biomedicine, pp 44–48 - Ziegenhain C, Vieth B, Parekh S, Reinius B,
Guillaumet-Adkins A, Smets M, Leonhardt H,
Heyn H, Hellmann I, Enard W (2017) Com-
parative analysis of single-cell RNA sequencing
methods. Mol Cell 65(4):631–643 - Gao S, Tian X, Chang H, Sun Y, Wu Z,
Cheng Z, Dong P, Zhao Q, Ruan J, Bu W
(2017) Two novel lncRNAs discovered in
human mitochondrial DNA using PacBio full-
length transcriptome data. Mitochondrion.
https://doi.org/10.1016/j.mito.2017.08.002 - Ilicic T, Kim JK, Kolodziejczyk AA, Bagger FO,
Mccarthy DJ, Marioni JC,TeichmannSA (2016)
Classification of low quality cells from single-cell
RNA-seq data. Genome Biol 17(1):29 - Anders S, Huber W (2010) Differential expres-
sion analysis for sequence count data. Genome
Biol 11(10):R106 - Robinson MD, Mccarthy DJ, Smyth GK (2010)
edgeR: a Bioconductor package for differential
expression analysis of digital gene expression
data. Bioinformatics 26(1):139–140 - Zhang Y, Li D, Sun B (2015) Do housekeeping
genes exist? PLoS One 10(5):e0123691 - Jiang L, Schlesinger F, Davis CA, Zhang Y,
Li R, Salit M, Gingeras TR, Oliver B (2011)
Synthetic spike-in standards for RNA-seq
experiments. Genome Res 21(9):1543–1551 - Risso D, Ngai J, Speed TP, Dudoit S (2014)
Normalization of RNA-seq data using factor
analysis of control genes or samples. Nat Bio-
technol 32(9):896–902 - Love ́n J, Orlando DA, Sigova AA, Lin CY, Rahl
PB, Burge CB, Levens DL, Lee TI, Young RA
(2012) Revisiting global gene expression anal-
ysis. Cell 151(3):476–482 - Islam S, Zeisel A, Joost S, La MG, Zajac P,
Kasper M, Lo ̈nnerberg P, Linnarsson S
(2014) Quantitative single-cell RNA-seq with
unique molecular identifiers. Nat Methods 11
(2):163–166 - Lun AT, Bach K, Marioni JC (2016) Pooling
across cells to normalize single-cell RNA
sequencing data with many zero counts.
Genome Biol 17(1):75 - Ren Y, Zhang J, Sun Y, Wu Z, Ruan J, He B,
Liu G, Gao S, Bu W (2016) Full-length tran-
scriptome sequencing on PacBio platform
(in Chinese). Chin Sci Bull 11(61):1250–1254 - Satija R, Farrell JA, Gennert D, Schier AF,
Regev A (2015) Spatial reconstruction of
single-cell gene expression data. Nat Biotech-
nol 33(5):495–502 - Wold S, Esbensen K, Geladi P (1987) Principal
component analysis. Chemometr Intell Lab
Syst 2(1–3):37–52 - Hyvarinen A, Oja E (2000) Independent com-
ponent analysis: algorithms and applications.
Neural Netw 13(4–5):411–430 - Balakrishnama S, Ganapathiraju A (1998) Lin-
ear discriminant analysis – a brief tutorial. Pro-
cof Intjoint Confon Neural Networks 3
(94):387–391 - Carroll JD, Arabie P (1980) Multidimensional
scaling. Annu Rev Psychol 31(31):607–649 - Maaten LVD, Hinton G (2008) Viualizing
data using t-SNE. J Mach Learn Res 9
(2605):2579–2605
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